{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25e46424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from statsmodels.graphics.tsaplots import plot_acf  #自相关图\n",
    "from statsmodels.graphics.tsaplots import plot_pacf    #偏自相关图\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox\n",
    "\n",
    "%pylab inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74508bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "sns.set(color_codes=True) #seaborn设置背景\n",
    "sns.set_style(\"darkgrid\")\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "pylab.rcParams['figure.figsize'] = (10, 6)   #设置输出图片大小\n",
    "sns.set(color_codes=True) #seaborn设置背景"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb1b0a7",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab2220f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区站号</th>\n",
       "      <th>纬度</th>\n",
       "      <th>经度</th>\n",
       "      <th>观测场海拔高度</th>\n",
       "      <th>年</th>\n",
       "      <th>月</th>\n",
       "      <th>日</th>\n",
       "      <th>20-8时降水量</th>\n",
       "      <th>8-20时降水量</th>\n",
       "      <th>20-20累计降水量</th>\n",
       "      <th>填充后20-8时降水量</th>\n",
       "      <th>填充后8-20时降水量</th>\n",
       "      <th>填充后20-20累计降水量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56778</td>\n",
       "      <td>2502</td>\n",
       "      <td>10243</td>\n",
       "      <td>18934</td>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56778</td>\n",
       "      <td>2502</td>\n",
       "      <td>10243</td>\n",
       "      <td>18934</td>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56778</td>\n",
       "      <td>2502</td>\n",
       "      <td>10243</td>\n",
       "      <td>18934</td>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56778</td>\n",
       "      <td>2502</td>\n",
       "      <td>10243</td>\n",
       "      <td>18934</td>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56778</td>\n",
       "      <td>2502</td>\n",
       "      <td>10243</td>\n",
       "      <td>18934</td>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     区站号    纬度     经度  观测场海拔高度     年  月  日  20-8时降水量  8-20时降水量  20-20累计降水量  \\\n",
       "0  56778  2502  10243    18934  1951  1  1       0.0       0.0         0.0   \n",
       "1  56778  2502  10243    18934  1951  1  2       0.0       0.0         0.0   \n",
       "2  56778  2502  10243    18934  1951  1  3       0.0       0.0         0.0   \n",
       "3  56778  2502  10243    18934  1951  1  4       0.0       0.0         0.0   \n",
       "4  56778  2502  10243    18934  1951  1  5       0.0       0.0         0.0   \n",
       "\n",
       "   填充后20-8时降水量  填充后8-20时降水量  填充后20-20累计降水量  \n",
       "0          0.0          0.0            0.0  \n",
       "1          0.0          0.0            0.0  \n",
       "2          0.0          0.0            0.0  \n",
       "3          0.0          0.0            0.0  \n",
       "4          0.0          0.0            0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('rain.xlsx', sheet_name=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f69a1f",
   "metadata": {},
   "source": [
    "### 去除无用列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e266656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1951</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  rainfall\n",
       "0  1951      1    1       0.0\n",
       "1  1951      1    2       0.0\n",
       "2  1951      1    3       0.0\n",
       "3  1951      1    4       0.0\n",
       "4  1951      1    5       0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['区站号','纬度','经度','观测场海拔高度','20-8时降水量','8-20时降水量','20-20累计降水量','填充后20-8时降水量','填充后8-20时降水量']\n",
    "data = data.drop(columns=labels)\n",
    "data.columns = ['year', 'month', 'day', 'rainfall']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ba4684",
   "metadata": {},
   "source": [
    "### 构建datetime索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b230aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1951     9499.1\n",
       "1952    11514.5\n",
       "1953     9189.6\n",
       "1954    13336.4\n",
       "1955    11633.1\n",
       "         ...   \n",
       "2016    11523.8\n",
       "2017    11864.0\n",
       "2018    10852.0\n",
       "2019     8403.0\n",
       "2020    10597.8\n",
       "Freq: A-DEC, Length: 70, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = data['year'][0]\n",
    "end = data['year'][len(data) - 1] \n",
    "rains = []\n",
    "for i in range(start, end+1):\n",
    "    idx = data['year'] == i\n",
    "    rain = data['rainfall'][idx].sum()\n",
    "    rains.append(rain)\n",
    "data = pd.Series(rains, index=pd.period_range(start, end, freq='Y'))\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55068c61",
   "metadata": {},
   "source": [
    "### 去掉2020年数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4fc988b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1951     9499.1\n",
       "1952    11514.5\n",
       "1953     9189.6\n",
       "1954    13336.4\n",
       "1955    11633.1\n",
       "         ...   \n",
       "2015    11965.5\n",
       "2016    11523.8\n",
       "2017    11864.0\n",
       "2018    10852.0\n",
       "2019     8403.0\n",
       "Freq: A-DEC, Length: 69, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[:-1]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb4610a",
   "metadata": {},
   "source": [
    "### 时序图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d742c23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd4b25b",
   "metadata": {},
   "source": [
    "### 自相关图和偏相关图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58c96f65",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(data)\n",
    "plot_pacf(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3082d9b1",
   "metadata": {},
   "source": [
    "### ADF测试，p-value<0.05，平稳序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab8c3096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -8.202523e+00\n",
      "p-value                        7.167031e-13\n",
      "#Lags Used                     0.000000e+00\n",
      "Number of Observations Used    6.800000e+01\n",
      "Critical Value (1%)           -3.530399e+00\n",
      "Critical Value (5%)           -2.905087e+00\n",
      "Critical Value (10%)          -2.590001e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def adf_test(timeseries):\n",
    "    print(\"Results of Dickey-Fuller Test:\")\n",
    "    dftest = adfuller(timeseries, autolag=\"AIC\")\n",
    "    dfoutput = pd.Series(\n",
    "        dftest[0:4],\n",
    "        index=[\n",
    "            \"Test Statistic\",\n",
    "            \"p-value\",\n",
    "            \"#Lags Used\",\n",
    "            \"Number of Observations Used\",\n",
    "        ],\n",
    "    )\n",
    "    for key, value in dftest[4].items():\n",
    "        dfoutput[\"Critical Value (%s)\" % key] = value\n",
    "    print(dfoutput)\n",
    "\n",
    "adf_test(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa672b8",
   "metadata": {},
   "source": [
    "### KPSS测试，p-value>0.05，平稳序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19fee524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of KPSS Test:\n",
      "Test Statistic           0.444992\n",
      "p-value                  0.057762\n",
      "Lags Used                2.000000\n",
      "Critical Value (10%)     0.347000\n",
      "Critical Value (5%)      0.463000\n",
      "Critical Value (2.5%)    0.574000\n",
      "Critical Value (1%)      0.739000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import kpss\n",
    "def kpss_test(timeseries):\n",
    "    print(\"Results of KPSS Test:\")\n",
    "    kpsstest = kpss(timeseries, regression=\"c\", nlags=\"auto\")\n",
    "    kpss_output = pd.Series(\n",
    "        kpsstest[0:3], index=[\"Test Statistic\", \"p-value\", \"Lags Used\"]\n",
    "    )\n",
    "    for key, value in kpsstest[3].items():\n",
    "        kpss_output[\"Critical Value (%s)\" % key] = value\n",
    "    print(kpss_output)\n",
    "    \n",
    "kpss_test(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf68fe2",
   "metadata": {},
   "source": [
    "### 作一阶差分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7147909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_diff = data.diff(1).dropna()\n",
    "data_diff.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d3ea1a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABtgOWdnZ2UpPT1dqaqoyMjLqzM/Ly9PIkSM1ePBg3XPPPSotLQ1KoQAAAM2JrZBVWFiouXPnavHixVqxYoUyMzO1c+dO/3zLsjR+/HiNGTNGK1eu1GWXXab58+cHvWgAAICmzlbIysnJUUpKimJjYxUVFaX+/ftr9erV/vlffPGFoqKi1LdvX0nSuHHjNGLEiOBWDAAA0AzYCllFRUVyuVz+6fj4eBUWFvqnd+/erfPOO08zZszQ0KFDNXPmTEVFRQWvWgAAgGYi3M7CPp9PDofDP21ZVsB0TU2NNm3apEWLFql79+764x//qGeeeUbPPPOMraLi4qJtLW9HRGSYJMnlamtsG80FPaAHEj2Q6IFEDyR6INEDKbg9sBWyEhMTtXnzZv90cXGx4uPjjynMpU6dOql79+6SpEGDBmnixIm2iyopOSyfz7K9Xn14qr2KiAxTcXG5kedvLlyutvSAHtAD0QOJHkj0QKIHkv0eOJ2OUx4YsjVc2Lt3b23cuFEHDhxQRUWF1q5d6z//SpKuuOIKHThwQF999ZUkad26derWrZudTQAAAIQEW0eyEhISNHnyZI0aNUoej0e33nqrkpOTNWbMGE2cOFHdu3fXvHnz9Mgjj6iiokKJiYmaM2eOqdoBAACaLFshS5LcbrfcbnfAYwsWLPD/3KNHDy1duvTsKwMAAGjGuOM7AACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGGA7ZGVnZys9PV2pqanKyMg46XLr16/XjTfeeFbFAQAANFfhdhYuLCzU3LlztWzZMkVGRmr48OG6+uqr1bVr14Dl9u/fr2effTaohQIAADQnto5k5eTkKCUlRbGxsYqKilL//v21evXqOss98sgj+s1vfhO0IgEAAJobWyGrqKhILpfLPx0fH6/CwsKAZd544w395Cc/UY8ePYJTIQAAQDNka7jQ5/PJ4XD4py3LCpj++uuvtXbtWr3++uvat2/fGRcVFxd9xuueTkRkmCTJ5WprbBvNBT2gBxI9kOiBRA8keiDRAym4PbAVshITE7V582b/dHFxseLj4/3Tq1evVnFxsW655RZ5PB4VFRXpjjvu0OLFi20VVVJyWD6fZWud+vJUexURGabi4nIjz99cuFxt6QE9oAeiBxI9kOiBRA8k+z1wOh2nPDBka7iwd+/e2rhxow4cOKCKigqtXbtWffv29c+fOHGi1qxZo6ysLM2fP1/x8fG2AxYAAEAosBWyEhISNHnyZI0aNUo333yzBg0apOTkZI0ZM0bbt283VSMAAECzY2u4UJLcbrfcbnfAYwsWLKiz3I9+9COtW7fuzCsDAABoxrjjOwAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAHhjV1Ac+H1WaryeBu7jKA5UFapo1U1jV1Go6IH9ECiBxI9kOiBFHo9aBURpjCno1FrIGTVU5XHq937yhu7jKCJrfTq0KGjjV1Go6IH9ECiBxI9kOiBFHo9uCCxraJaNW7MYbgQAADAANshKzs7W+np6UpNTVVGRkad+R988IGGDBmiwYMH6/7771dpaWlQCgUAAGhObIWswsJCzZ07V4sXL9aKFSuUmZmpnTt3+ucfPnxYjz/+uObPn6+VK1fqkksu0Ysvvhj0ogEAAJo6WyErJydHKSkpio2NVVRUlPr376/Vq1f753s8Hs2cOVMJCQmSpEsuuUQFBQXBrRgAAKAZsBWyioqK5HK5/NPx8fEqLCz0T7dr1079+vWTJFVWVmr+/Pn6xS9+EaRSAQAAmg9bp937fD45HP+5HNKyrIDpWuXl5ZowYYIuvfRSDR061HZRcXHRttepr4jIMEmSy9XW1noHyioVWxk6t3CQpNjYqMYuodHRA3og0QOJHkj0QAqtHrSNaaP2Ma1tr2c3H5yKrZCVmJiozZs3+6eLi4sVHx8fsExRUZHuuecepaSkaMaMGWdUVEnJYfl81hmtezqeaq8iIsNUXGzvdgxHq2pC6tLW2NiokHo9Z4Ie0AOJHkj0QKIHUuj1IKZ1mLxVHlvruFxtbeUDp9NxygNDtoYLe/furY0bN+rAgQOqqKjQ2rVr1bdvX/98r9ercePGacCAAXr44YdPeJQLAACgJbB1JCshIUGTJ0/WqFGj5PF4dOuttyo5OVljxozRxIkTtW/fPv3jH/+Q1+vVmjVrJEmXX365Zs2aZaR4AACApsr2rVDdbrfcbnfAYwsWLJAkde/eXV999VVwKgMAAGjGuOM7AACAAYQsAAAAAwhZAAAABhCyAAAADLB94jvQ3Pl8lvIKylS2s0QxbcLVJSlGTie3GwEABBchCy2Kz2dpyfqdKig5Kk+NTxHhTiXFRemX13claAEAgorhQrQoeQVl/oAlSZ4anwpKjiqvoKyRKwMAhBpCFlqUooMV/oBVy1PjU9HBikaqCKHO57O08/tS5ezYp53flxr7yjAATQ/DhWhR4tu1UUS4MyBoRYQ7Fd+uTSNWhVDF8DTQsnEkCy1Kl6QYJcVFqfbtrfZNr0tSTKPWhdDE8DTQshGy0KI4nQ798vquiju3tWLbtpK794UcVYAxDE8DLRvDhWhxnE6H2rQKV3i4U107nNvY5SCEMTwNtGwcyQIAQxieBlo2QhYAGHLs8HTMOZEMTwMtDMOFAGBQ7fB0m1ZieBpoYTiSBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMsB2ysrOzlZ6ertTUVGVkZNSZ/+WXX2rYsGHq37+/Hn74YdXU1ASlUAAA0LL4fJZ2fl+qnB37tPP7Uvl8VmOXZIutkFVYWKi5c+dq8eLFWrFihTIzM7Vz586AZaZMmaLHHntMa9askWVZWrJkSVALBgAAoc/ns7Rk/U5l5+Trk+0Fys7J15L1O5tV0LIVsnJycpSSkqLY2FhFRUWpf//+Wr16tX/+999/r8rKSvXs2VOSNGzYsID5AAAA9ZFXUKaCkqPy1PgkSZ4anwpKjiqvoKyRK6u/cDsLFxUVyeVy+afj4+OVm5t70vkul0uFhYW2i3o5a4cOllXZXq8+dheVy+Fw6NmMv9taz2tZqqr2GqmpMYSHO1Xzw47bEhUdPCqHw6E3P/ymsUtpVC19P5AapgdFB49KUpPd39gP6IHU9HpQeqTaH7BqeWp8+mDLHn32VdFp128VGaYwh8PWNiMiw+Sx8V7fLqaVHh6dctL5tkKWz+eT45iCLcsKmD7d/PoKjwhTRGSY7fXq46IfxZ7Reo4an7zh9T9EWbD/iCQp6bxzzmh7DbGd8PD6H8hsDq/HjvNd0Uafv1Zz6FtL3g/Y3/6D/YDtSE1rP2jTKkzlRyXrmLdeh+Pfj9enzvAwp63XU8tO/giPOPWytkJWYmKiNm/e7J8uLi5WfHx8wPzi4mL/9P79+wPm19e96ZcZHXN1udqquLjc1jpHq2q0e1/916n9xHrbdRfZ2o5dZ7qd2NgoHTp01Ph27Gqo7Uj2e3AmmnrfWvp+8OaH3yg83Nkg+1tDYD848+00xH7Q1PvW1PaD2nOyaocMI8KdSoqL0i+v7yqn8/QHcC5IbKuoVrZiju18cLo6bEW83r17a+PGjTpw4IAqKiq0du1a9e3b1z+/Q4cOatWqlbZs2SJJysrKCpgPAABQH06nQ7+8vqvcvS9Un+5Jcve+sN4Bq6mwFbISEhI0efJkjRo1SjfffLMGDRqk5ORkjRkzRtu3b5ckPf/883r66aeVlpamo0ePatSoUUYKBxCafD5LFVU1Kj1S3Swv2QYaU6j9/jidDnXtcK56X56orh3ObVYBS7I5XChJbrdbbrc74LEFCxb4f7700ku1dOnSs68MQItTOzxQUlopS1J2Tr6t4QGgJeP3p+nhju8AmozaS7ZrP3ubvGS79hP/wfKqkPjEDzTk7w/qh5AFoMkoOlhxwku2iw5WBHU7x37iP1Re1Sxvcggcr6F+f1B/hCwATUZ8uzaKOO6S64hwp+LbtQnqdvjEj1DUUL8/qD9CFoAmo0tSjJLiovxvFLWXbHdJignqdvjEj1oNNWzcECekN9TvD+rP9onvAGBK7SXbeQVlKjpYofh2bdQlKSboJ+3WfuI/Nmjxib/laagTxRtqOw31+4P6I2QBaFJqL9nu2uFcY9uo/cR//E0O+cTfspxq2DiY+19DbUdqmN8f1B8hC0CLc+wn/rLKGsW0DucTfwt0qmHjYIaUhtoOmh5CFoAWqfYTf0N8vRKapoYaNmZ4uuXixHcAQIvUUCeKc0J6y8WRLABAi9RQw8ackN5yEbIAAPVWeyuC6hqfdn5f2uzDQkMNG3NCesvEcCEAoF6OvRVB2ZFq7pQPnAYhCwBQL9wpH7CHkAUAqBfulA/YQ8gCANQL340H2EPIAgDUS0PeiqAhvusPMI2rCwEA9dJQtyJoqO/6A0zjSBYAoN5qb0XQ+/JEde1wrpHQwwn2TR9HGuuHkAUAaFI4wb5p41Ye9UfIAgA0KZxg37RxpLH+CFkAgCaF7/pr2jjSWH+c+A4AaFL4rr+mrfZI47FBiyONJ0bIAgA0OXzXX9NVe6SxoOSoPDU+jjSeAiELpxVqXwgLhCJ+T9FQONJYf4QsnBL3q0Et3sSbLn5P0dA40lg/nPiOU+IqEkhcst3U8XsKNE2ELJxSQ15Fws3tmi7exJs2rvYCmiZCFk6poe5Xw5GSpo038aaN+0oBTZOtkGVZlp599lmlpaUpPT1dW7ZsOeFyL730kgYOHKiBAwdqzpw5QSkUjaOh7lfDkZKmjTfxpo37SgFNk60T39esWaNvv/1Wq1at0q5duzR27FitWrVK4eH/eZqcnBx98sknWr58uRwOh+699169//776tevX9CLh3kNdRXJqY6UcGJl4+OS7aaNq72ApslWyNqwYYPS09PldDrVuXNnJSUlaevWrfrZz37mX8blcmnatGmKjIyUJF100UXau3dvcKtGg2qIq0i4uV3Txpt408fVXkDTYytkFRUVKT4+3j/tcrm0b9++gGUuvvhi/8/5+fl677339Oabb55lmQh1HClp+ngTBwB7bIUsn88nh+M/n1wty5LTeeLTur755huNHTtWDz30kC688EJbRcXFRdta/ky4XG1tLX+grFKxld56Lx/+w7kRsbFRtrZj19lsx3Rtdt13c7K+3n1Qe0uO6Py4c/TjC9oZP1LSlP9/Gmo7TW0/aAz0gB5I9EAKrR60jWmj9jGtba9nNx+cymlD1gsvvKB169ZJksrLy1VUVOSft3///oAjW7W2bNmiiRMnasaMGRo4cKDtokpKDhu9qszlaqvi4nJb6xytqtGhQ0frvXzND8NedtY5E2e6ndjYKOO1nYnE2NZKjP33L0VZmdkr10z3wOezVH6kWtU1Pm3+osDo8Fqo7QcNiR7QA4keSKHXg5jWYfJWeWytYzcfOJ2OUx4YOm3ImjRpkiZNmiRJeu+99/TOO+9o0KBB2rNnj/Lz89W9e/eA5QsKCjRhwgTNnTtXvXr1qnehQCjhDtwAAFvDhWlpacrNzdXgwYMlSbNmzVLr1q1VWFio++67T1lZWXr11VdVVVWlZ555xr/e8OHD9atf/Sq4lQNN2KluScE5TQDQMtgKWQ6HQ1OnTtXUqVMDHk9ISFBWVpYk6ZFHHtEjjzwSvAqBZohbUgAAuOM7YAA37wQAELIAA7gDNwDA1nBhS9YqIkwXJNb/ss5WkWGSZGudM3Gm22kb00YxrcNMlNRsmO7Bg8Ov0Bf5B/R98WF1cEWr24XtjZ30zn5w5ugBPZDogRR6PWgV0fivhZBVT2FOh6Ja1b9dYT/cT8zOOmfiTLfTPqa17UtbQ01D9ODqyxKkyxKMbkNiPzgb9IAeSPRAogcmMFwINHM+n6XDFR7tL63Utp37jd5jDgBQf4QsoBnz+Sz9IXOb9pYcUUlZpV7J+kJ/yNxG0AKAJoCQBTRjuXklyttbJuuHTFXl8Spvb5ly80oatzAAACELaM52F5ar2hP4nZrVHq++K7T3tVEAgOAjZAHN2AUJbRV53BU0kRFh6phg9qpWAMDpEbKAZiy5S5y6nB+jVhFhcujflyx3OT9GyV3iGrs0AGjxuIUD0Iw5nQ797vaeys0r0XeF5eqY0FbJXeL4EmoAaAIIWUAz53Q61LPreerZ9bzGLgUAcAyGCwEAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADLAVsizL0rPPPqu0tDSlp6dry5Ytp1z+2Wef1bRp086qQJycz2fpcIVH+0srtW3nfvl8VmOXBAAAfmArZK1Zs0bffvutVq1apXnz5mn69Omqqak54bIbN27U8uXLg1Ik6vL5LP0hc5v2lhxRSVmlXsn6Qn/I3EbQAgCgibAVsjZs2KD09HQ5nU517txZSUlJ2rp1a53lDh06pLlz52rcuHFBKxSBcvNKlLe3TNYPmarK41Xe3jLl5pU0bmEAAECSzZBVVFSk+Ph4/7TL5dK+ffvqLPfYY49p8uTJiomJOfsKcUK7C8tV7fEGPFbt8eq7wvJGqggAABwr3M7CPp9PDofDP21ZlpzOwJz29ttvKykpSb169dKyZcvOqKi4uOgzWs8Ol6ut0eePiAwzup3uP47X6r/tVmX1f4JWq8gwXf7j+Hpv03QPmgN6QA8keiDRA4keSPRACm4PThuyXnjhBa1bt06SVF5erqKiIv+8/fv3BxzZkqRVq1apuLhYQ4YMUWlpqY4eParZs2drxowZ9S6qpOSw0XOLXK62Ki42e8TH80P4MbWdTnFR6pwUo7y9Zar2eBUZEabOSTHqFBdVr202RA+aOnpADyR6INEDiR5I9ECy3wOn03HKA0OnDVmTJk3SpEmTJEnvvfee3nnnHQ0aNEh79uxRfn6+unfvHrD8a6+95v952bJl2rRpk62AhfpxOh363e09lZtXou8Ky9Uxoa2Su8TJ6XScfmUAAGCcreHCtLQ05ebmavDgwZKkWbNmqXXr1iosLNR9992nrKwsI0XixJxOh3p2PU89u57X2KUAAIDj2ApZDodDU6dO1dSpUwMeT0hIOGHAGjZsmIYNG3Z2FQIAADRD3PEdAADAAEIWAACAAYQsAAAAAwhZAAAABhCyAAAADCBkAQAAGEDIAgAAMICQBQAAYAAhCwAAwABCFgAAgAGELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMAAQhYAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEKWAT6fpcMVHu0vrdS2nfvl81mNXRIAAGhghKwg8/ks/SFzm/aWHFFJWaVeyfpCf8jcRtACAKCFIWQFWW5eifL2lsn6IVNVebzK21um3LySxi0MAAA0KFshy7IsPfvss0pLS1N6erq2bNlywuXWrVunYcOGacCAAXrqqaeCUmhzsbuwXNUeb8Bj1R6vvissb6SKAABAY7AVstasWaNvv/1Wq1at0rx58zR9+nTV1NQELPPdd99p5syZ+tOf/qSVK1fqH//4hzZs2BDUopuyCxLaKjIiLOCxyIgwdUxo20gVAQCAxmArZG3YsEHp6elyOp3q3LmzkpKStHXr1oBl3n//faWnpysxMVERERGaO3euevToEdSim7LkLnHqcn6MWkWEySGpVUSYupwfo+QucY1dGgAAaEDhdhYuKipSfHy8f9rlcmnfvn0By+zatUsREREaN26cCgoKdP311+uBBx4ISrHNgdPp0O9u76ncvBJ9V1iujgltldwlTk6no7FLAwAADchWyPL5fHI4/hMWLMuS0xl4MMzr9Wrz5s1auHChoqKiNH78eC1fvlzDhg2r93bi4qLtlHVGXC6zw3f9EmKMPn8wmO5Bc0AP6IFEDyR6INEDiR5Iwe3BaUPWCy+8oHXr1kmSysvLVVRU5J+3f//+gCNbknTeeeepV69eat++vSTpF7/4hXJzc22FrJKSw0ZveeBytVVxccs+EZ0e0AOJHkj0QKIHEj2Q6IFkvwdOp+OUB4ZOe07WpEmTlJWVpaysLE2ZMkXZ2dnyer3atWuX8vPz1b1794Dlb7jhBn3yyScqKyuT1+vVxx9/rG7dutW7YAAAgFBga7gwLS1Nubm5Gjx4sCRp1qxZat26tQoLC3XfffcpKytLPXr00L333qs77rhDHo9H11xzjW655RYjxQMAADRVDsuymtytyBkuNI8e0AOJHkj0QKIHEj2Q6IHUCMOFAAAAsI+QBQAAYAAhCwAAwABCFgAAgAGELAAAAANs3cKhoTTEV9DwNTf0QKIHEj2Q6IFEDyR6INEDyV4PTrdsk7yFAwAAQHPHcCEAAIABhCwAAAADCFkAAAAGELIAAAAMIGQBAAAYQMgCAAAwgJAFAABgACELAADAAEIWAACAASEdsrKzs5Wenq7U1FRlZGTUmf/ll19q2LBh6t+/vx5++GHV1NQ0QpVmvfTSSxo4cKAGDhyoOXPmnHD+DTfcoCFDhmjIkCEn7FNzN3LkSA0cOND/Gj///POA+aG+H7z99tv+1z5kyBBdeeWVeuKJJwKWCeX94PDhwxo0aJD27NkjScrJyZHb7VZqaqrmzp17wnX27t2rESNGKC0tTePHj9eRI0casuSgO74HmZmZGjRokNxut6ZPn67q6uo66yxfvlx9+vTx7xMn61VzcXwPpk+frtTUVP/re//99+usE8r7wYYNGwL+LqSkpGjs2LF11gml/eBE74fG/x5YIWrfvn3WDTfcYB08eNA6cuSI5Xa7rW+++SZgmYEDB1pbt261LMuypk+fbmVkZDRCpeZ8+umn1u23325VVVVZ1dXV1qhRo6y1a9cGLDN27Fjr73//eyNVaJ7P57P69OljeTyeky4T6vvBsb7++murX79+VklJScDjobofbNu2zRo0aJDVrVs367vvvrMqKiqs6667ztq9e7fl8Xis0aNHW+vXr6+z3n333Wf95S9/sSzLsl566SVrzpw5DV160Bzfg7y8PKtfv35WeXm55fP5rIceesh67bXX6qz3xBNPWNnZ2Q1fsAHH98CyLGvQoEFWYWHhKdcL5f3gWEVFRdZNN91k/etf/6qzXqjsByd6P8zOzjb+9yBkj2Tl5OQoJSVFsbGxioqKUv/+/bV69Wr//O+//16VlZXq2bOnJGnYsGEB80OBy+XStGnTFBkZqYiICF100UXau3dvwDI7duzQK6+8IrfbrSeeeEJVVVWNVK0ZeXl5kqTRo0dr8ODBWrRoUcD8lrAfHOvxxx/X5MmT1b59+4DHQ3U/WLJkiWbOnKn4+HhJUm5urjp16qSOHTsqPDxcbre7zv+3x+PRZ599pv79+0tq/vvE8T2IjIzUzJkzFR0dLYfDoR//+Md1/i5I0vbt27V8+XK53W49+OCDKi0tbejSg+b4HlRUVGjv3r2aMWOG3G63/vu//1s+ny9gnVDfD441Z84cDR8+XBdeeGGdeaGyH5zo/TA/P9/434OQDVlFRUVyuVz+6fj4eBUWFp50vsvlCpgfCi6++GJ/eMjPz9d7772n6667zj//yJEjuuyyyzRlyhQtX75cZWVl+tOf/tRI1ZpRVlamXr16ad68eXr99df11ltv6dNPP/XPbwn7Qa2cnBxVVlZqwIABAY+H8n4wa9YsXXXVVf7p0/1dkKSDBw8qOjpa4eHhkpr/PnF8Dzp06KBrrrlGknTgwAFlZGTopptuqrOey+XS/fffr5UrVyopKanOEHNzcnwP9u/fr5SUFM2ePVtLlizR5s2btXTp0oB1Qn0/qJWfn69NmzZp1KhRJ1wvVPaDE70fOhwO438PQjZk+Xw+ORwO/7RlWQHTp5sfSr755huNHj1aDz30UMAnlXPOOUcLFizQRRddpPDwcI0ePVobNmxovEINuOKKKzRnzhy1bdtW7du316233hrwGlvSfvDWW2/p7rvvrvN4S9gPatXn//tEj4XiPlFYWKi77rpLt9xyi66++uo68+fNm6crr7xSDodD9957rz7++ONGqNKMjh07at68eYqPj1ebNm00cuTIOvt8S9kPMjMzdccddygyMvKE80NtPzj2/bBjx47G/x6EbMhKTExUcXGxf7q4uDjgMOnx8/fv33/Cw6jN3ZYtW/TrX/9av/vd7zR06NCAeXv37g349GZZlj+th4rNmzdr48aN/unjX2NL2Q+qq6v12Wef6cYbb6wzryXsB7VO93dBktq3b6/y8nJ5vd6TLtPcffvttxo+fLiGDh2qCRMm1JlfXl6u119/3T9tWZbCwsIasEKz/vnPf2rNmjX+6RPt8y1hP5CkDz/8UOnp6SecF2r7wfHvhw3x9yBkQ1bv3r21ceNGHThwQBUVFVq7dq369u3rn9+hQwe1atVKW7ZskSRlZWUFzA8FBQUFmjBhgp5//nkNHDiwzvzWrVvrueee03fffSfLspSRkaF+/fo1QqXmlJeXa86cOaqqqtLhw4e1fPnygNfYEvYD6d9vKhdeeKGioqLqzGsJ+0GtHj166F//+pd27dolr9erv/zlL3X+vyMiInTVVVdp1apVkqQVK1aE1D5x+PBh3XPPPZo0aZJGjx59wmWioqL0v//7v/4rcRctWhRS+4RlWZo9e7ZKS0vl8XiUmZlZ5/WF+n4g/Xu4uLKyUh07djzh/FDaD070ftggfw/O9Ez95mDlypXWwIEDrdTUVGv+/PmWZVnWvffea+Xm5lqWZVlffvmldcstt1j9+/e3fvvb31pVVVWNWW7QPfnkk1bPnj2twYMH+/8tXrw4oAerV6/292jatGkh1wPLsqy5c+daaWlpVmpqqvX6669bltWy9gPLsqx3333XeuCBBwIea0n7wQ033OC/oionJ8dyu91WamqqNWvWLMvn81mWZVkzZsywPvjgA8uyLGvPnj3WnXfeaQ0YMMAaPXq0dejQoUarPVhqe/Daa69Z3bp1C/i78Mc//tGyrMAefPbZZ9bNN99spaWlWePGjbPKysoas/ygOHY/WLRokTVgwACrX79+1nPPPedfpqXsB5ZlWZ9//rl122231VkmFPeDk70fmv574LAsywpaVAQAAICkEB4uBAAAaEyELAAAAAMIWQAAAAYQsgAAAAwgZAEAABhAyAIAADCAkAUAAGAAIQsAAMCA/weWm6ueqHrXtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(data_diff)\n",
    "plot_pacf(data_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2363c96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -5.041014\n",
      "p-value                         0.000018\n",
      "#Lags Used                      6.000000\n",
      "Number of Observations Used    61.000000\n",
      "Critical Value (1%)            -3.542413\n",
      "Critical Value (5%)            -2.910236\n",
      "Critical Value (10%)           -2.592745\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "adf_test(data_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "796380c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of KPSS Test:\n",
      "Test Statistic            0.500000\n",
      "p-value                   0.041667\n",
      "Lags Used                67.000000\n",
      "Critical Value (10%)      0.347000\n",
      "Critical Value (5%)       0.463000\n",
      "Critical Value (2.5%)     0.574000\n",
      "Critical Value (1%)       0.739000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "kpss_test(data_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "550732ca",
   "metadata": {},
   "source": [
    "## ARIMA模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5778e0b9",
   "metadata": {},
   "source": [
    "### 训练和预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e80c4ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIC最小的p值和q值为：0、1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\IDE\\Miniconda\\envs\\ml\\lib\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "pmax = int(len(data)/10)\n",
    "qmax = int(len(data)/10)\n",
    "bic_matrix = [] #bic矩阵\n",
    "for p in range(pmax+1):\n",
    "    tmp = []\n",
    "    for q in range(qmax+1):\n",
    "        try: #存在部分报错，所以用try来跳过报错。\n",
    "            tmp.append(ARIMA(data, order=(p,1,q)).fit().bic)\n",
    "        except:\n",
    "            tmp.append(None)\n",
    "    bic_matrix.append(tmp)\n",
    "\n",
    "bic_matrix = pd.DataFrame(bic_matrix) #从中可以找出最小值\n",
    "\n",
    "p,q = bic_matrix.stack().idxmin() #先用stack展平，然后用idxmin找出最小值位置。\n",
    "print(u'BIC最小的p值和q值为：%s、%s' %(p,q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93ec77e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    10286.055686\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(data, order=(p,1,q)).fit() \n",
    "data_predict = model.forecast(1)\n",
    "data_predict"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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